What about healthcare must change for analytics to flourish?

The amount of information about a patient continues to increase and its sources are likewise increasing, which in part is driving a growing interest in the use of data analytics in the field of healthcare. Looking at the successful application of these tools and services in other industries such as financial and retail services, many organizations in and around healthcare are coming two terms with what big data and analytics can do for providers and patients.

“Think of the power of that in healthcare where you’re talking about somebody’s health, which is by definition a very data-intensive endeavor,” says Tom Olenzak, Director of Innovation at Independence Blue Cross. “You think about the amount of data that is generated around your health — not just doctor’s visits — claim history, labs, scripts (both filled and unfilled), diagnoses, and even things that are not thought of as traditional healthcare.”

This optimism, however, has yet to be transformed into widespread use of healthcare analytics. The chief stumbling block in the way of the healthcare industry making use of this technology remains getting the necessary information together in a central place as well as in the right format to apply statistical analysis.

“In healthcare, there has never been anybody who has pulled the pieces together,” explains Olenzak. “Historically, there hasn’t even been anybody who could aggregate claims and clinical data in one place and labs from somewhere else and scripts in still a third place.”

Those technological barriers aren’t the only hurdles that need to be crossed in healthcare. Even with the technology in hand, healthcare organizations and providers face the challenge of fitting it into clinical workflows.

“You look at your average provider — whether it’s a primary care physician or a specialist — and with everything going on there, there so overworked that any new wrinkled that you introduce into the workflow is going to be an issue,” adds Olenzak.

The challenge of clinical adoption of healthcare analytics necessarily leads to a conversation about payment and liability, the two needing to be aligned for healthcare organizations and provider to even consider making use of these tools and services.

“We wanted to feed blood glucose levels on a real-time basis to a physician,” continues Olenzak, “but they’re not set up to receive that data, they’re not set up to react real time because that’s not how a physician’s practice works, and there are legal issues that once they get that data if they haven’t appropriately acted on them they could be liable. And they’re not paid to work with data in real time.”

So what needs to change in healthcare for analytics to flourish? According to Olenzak, it’s the culture that needs to change. The industry needs to think differently about how its partners, from providers to payers, work together to improve patient outcomes and the cost of high-quality care.

“Unfortunately, the way our health system is designed, it has sometimes pitted payers against health care providers. We have to be able to break outside of that,” he observes. “We have to be able to break outside of that and work together with physicians and hospitals to create an environment where we can build a reimbursement model that focuses on the good of the patient.”

The task for health plans like his own, argues Olenzak, is to move from “a transaction-processing-based company to a manager of care” and in doing so making itself a valuable and more importantly trusted player in aggregating and sharing actionable data among healthcare organizations and providers.

“We are the one group in this very fragmented healthcare system that kind of sits at the hub, not just because of our place in the center of the financial transaction but because we are in a position to become not just the aggregator or intermediaries for the transaction but also for the data,” he argues. “It has to come from so many different places in so many different forms, and somebody needs to be that single source of truth, that quarterback, that can translate and get it to where it needs to be.”

Part of the problem of aggregating claims, clinical, labs and scripts data is that it comes in different forms, from different databases and has different naming standards. Organizations first need to understand who has access to that data (and then think about who SHOULD have access to it) and then they may want to de-identify PHI to keep the data secure. One key here is to consistently de-identify data from each contributor (and no PHI should leave any of the contributors during that process).